Overview

Dataset statistics

Number of variables17
Number of observations2017
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory268.0 KiB
Average record size in memory136.1 B

Variable types

Numeric12
Categorical5

Alerts

song_title has a high cardinality: 1956 distinct values High cardinality
artist has a high cardinality: 1343 distinct values High cardinality
id is highly correlated with targetHigh correlation
energy is highly correlated with loudnessHigh correlation
loudness is highly correlated with energyHigh correlation
target is highly correlated with idHigh correlation
id is highly correlated with targetHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
target is highly correlated with idHigh correlation
id is highly correlated with targetHigh correlation
energy is highly correlated with loudnessHigh correlation
loudness is highly correlated with energyHigh correlation
target is highly correlated with idHigh correlation
id is highly correlated with energy and 1 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with id and 2 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
target is highly correlated with idHigh correlation
id is uniformly distributed Uniform
song_title is uniformly distributed Uniform
id has unique values Unique
instrumentalness has 693 (34.4%) zeros Zeros
key has 216 (10.7%) zeros Zeros

Reproduction

Analysis started2022-03-22 21:40:28.323864
Analysis finished2022-03-22 21:41:09.838635
Duration41.51 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2017
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1008
Minimum0
Maximum2016
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:10.070851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100.8
Q1504
median1008
Q31512
95-th percentile1915.2
Maximum2016
Range2016
Interquartile range (IQR)1008

Descriptive statistics

Standard deviation582.4020662
Coefficient of variation (CV)0.5777798275
Kurtosis-1.2
Mean1008
Median Absolute Deviation (MAD)504
Skewness0
Sum2033136
Variance339192.1667
MonotonicityStrictly increasing
2022-03-22T18:41:10.363165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
13401
 
< 0.1%
13531
 
< 0.1%
13521
 
< 0.1%
13511
 
< 0.1%
13501
 
< 0.1%
13491
 
< 0.1%
13481
 
< 0.1%
13471
 
< 0.1%
13461
 
< 0.1%
Other values (2007)2007
99.5%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
20161
< 0.1%
20151
< 0.1%
20141
< 0.1%
20131
< 0.1%
20121
< 0.1%
20111
< 0.1%
20101
< 0.1%
20091
< 0.1%
20081
< 0.1%
20071
< 0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1394
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1875900344
Minimum2.84 × 10-6
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:10.653582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.84 × 10-6
5-th percentile0.000298
Q10.00963
median0.0633
Q30.265
95-th percentile0.8462
Maximum0.995
Range0.99499716
Interquartile range (IQR)0.25537

Descriptive statistics

Standard deviation0.2599892598
Coefficient of variation (CV)1.385943878
Kurtosis1.745620963
Mean0.1875900344
Median Absolute Deviation (MAD)0.06141
Skewness1.658392712
Sum378.3690994
Variance0.0675944152
MonotonicityNot monotonic
2022-03-22T18:41:10.927250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1198
 
0.4%
0.1146
 
0.3%
0.2736
 
0.3%
0.1356
 
0.3%
0.166
 
0.3%
0.01076
 
0.3%
0.01175
 
0.2%
0.01935
 
0.2%
0.1015
 
0.2%
0.1225
 
0.2%
Other values (1384)1959
97.1%
ValueCountFrequency (%)
2.84 × 10-61
< 0.1%
4.61 × 10-61
< 0.1%
4.69 × 10-61
< 0.1%
5.41 × 10-61
< 0.1%
7.94 × 10-61
< 0.1%
8.35 × 10-61
< 0.1%
8.8 × 10-61
< 0.1%
9.19 × 10-61
< 0.1%
1.01 × 10-51
< 0.1%
1.2 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9952
0.1%
0.9941
< 0.1%
0.9931
< 0.1%
0.9921
< 0.1%
0.9911
< 0.1%
0.992
0.1%
0.9881
< 0.1%
0.9862
0.1%
0.9831
< 0.1%
0.9782
0.1%

danceability
Real number (ℝ≥0)

Distinct632
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6184219137
Minimum0.122
Maximum0.984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:11.200192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.122
5-th percentile0.3268
Q10.514
median0.631
Q30.738
95-th percentile0.858
Maximum0.984
Range0.862
Interquartile range (IQR)0.224

Descriptive statistics

Standard deviation0.1610289674
Coefficient of variation (CV)0.2603869039
Kurtosis-0.2075123034
Mean0.6184219137
Median Absolute Deviation (MAD)0.111
Skewness-0.4196095843
Sum1247.357
Variance0.02593032835
MonotonicityNot monotonic
2022-03-22T18:41:11.461174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.68312
 
0.6%
0.7611
 
0.5%
0.70411
 
0.5%
0.64610
 
0.5%
0.61410
 
0.5%
0.63310
 
0.5%
0.6889
 
0.4%
0.6249
 
0.4%
0.5979
 
0.4%
0.7269
 
0.4%
Other values (622)1917
95.0%
ValueCountFrequency (%)
0.1221
< 0.1%
0.1231
< 0.1%
0.1481
< 0.1%
0.1521
< 0.1%
0.1561
< 0.1%
0.1622
0.1%
0.1641
< 0.1%
0.171
< 0.1%
0.1732
0.1%
0.1761
< 0.1%
ValueCountFrequency (%)
0.9841
< 0.1%
0.9671
< 0.1%
0.9621
< 0.1%
0.9592
0.1%
0.951
< 0.1%
0.9471
< 0.1%
0.9441
< 0.1%
0.9412
0.1%
0.9371
< 0.1%
0.9321
< 0.1%

duration_ms
Real number (ℝ≥0)

Distinct1921
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246306.1973
Minimum16042
Maximum1004627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:11.731550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum16042
5-th percentile156863.2
Q1200015
median229261
Q3270333
95-th percentile393475
Maximum1004627
Range988585
Interquartile range (IQR)70318

Descriptive statistics

Standard deviation81981.81422
Coefficient of variation (CV)0.3328451136
Kurtosis11.90242311
Mean246306.1973
Median Absolute Deviation (MAD)32907
Skewness2.499011859
Sum496799600
Variance6721017863
MonotonicityNot monotonic
2022-03-22T18:41:12.015620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1920005
 
0.2%
2434933
 
0.1%
2060133
 
0.1%
2284673
 
0.1%
2400003
 
0.1%
1893332
 
0.1%
2520532
 
0.1%
2438002
 
0.1%
1999732
 
0.1%
2543602
 
0.1%
Other values (1911)1990
98.7%
ValueCountFrequency (%)
160421
< 0.1%
165881
< 0.1%
475471
< 0.1%
515471
< 0.1%
520061
< 0.1%
667621
< 0.1%
822931
< 0.1%
828931
< 0.1%
877051
< 0.1%
932671
< 0.1%
ValueCountFrequency (%)
10046271
< 0.1%
8499601
< 0.1%
8250271
< 0.1%
7840131
< 0.1%
7634671
< 0.1%
7456531
< 0.1%
7031071
< 0.1%
7000271
< 0.1%
6595601
< 0.1%
6494151
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct719
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6815771443
Minimum0.0148
Maximum0.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:12.296463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0148
5-th percentile0.2538
Q10.563
median0.715
Q30.846
95-th percentile0.952
Maximum0.998
Range0.9832
Interquartile range (IQR)0.283

Descriptive statistics

Standard deviation0.2102730089
Coefficient of variation (CV)0.3085094779
Kurtosis0.5726185439
Mean0.6815771443
Median Absolute Deviation (MAD)0.139
Skewness-0.9130104652
Sum1374.7411
Variance0.04421473828
MonotonicityNot monotonic
2022-03-22T18:41:12.709122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85710
 
0.5%
0.6579
 
0.4%
0.869
 
0.4%
0.779
 
0.4%
0.7939
 
0.4%
0.8118
 
0.4%
0.8028
 
0.4%
0.9218
 
0.4%
0.8948
 
0.4%
0.6648
 
0.4%
Other values (709)1931
95.7%
ValueCountFrequency (%)
0.01481
< 0.1%
0.01561
< 0.1%
0.01611
< 0.1%
0.0231
< 0.1%
0.02881
< 0.1%
0.02911
< 0.1%
0.02951
< 0.1%
0.03021
< 0.1%
0.0311
< 0.1%
0.03471
< 0.1%
ValueCountFrequency (%)
0.9981
 
< 0.1%
0.9971
 
< 0.1%
0.9941
 
< 0.1%
0.9931
 
< 0.1%
0.9923
0.1%
0.9911
 
< 0.1%
0.992
 
0.1%
0.9895
0.2%
0.9881
 
< 0.1%
0.9863
0.1%

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct1107
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1332855286
Minimum0
Maximum0.976
Zeros693
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:12.985573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.62 × 10-5
Q30.054
95-th percentile0.8522
Maximum0.976
Range0.976
Interquartile range (IQR)0.054

Descriptive statistics

Standard deviation0.2731621791
Coefficient of variation (CV)2.049451144
Kurtosis2.261179811
Mean0.1332855286
Median Absolute Deviation (MAD)7.62 × 10-5
Skewness1.952755178
Sum268.8369112
Variance0.07461757612
MonotonicityNot monotonic
2022-03-22T18:41:13.262057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0693
34.4%
0.8975
 
0.2%
0.8784
 
0.2%
0.4253
 
0.1%
0.001283
 
0.1%
0.01073
 
0.1%
0.6253
 
0.1%
1.54 × 10-63
 
0.1%
0.6983
 
0.1%
0.8843
 
0.1%
Other values (1097)1294
64.2%
ValueCountFrequency (%)
0693
34.4%
1 × 10-61
 
< 0.1%
1.01 × 10-61
 
< 0.1%
1.03 × 10-62
 
0.1%
1.04 × 10-62
 
0.1%
1.07 × 10-61
 
< 0.1%
1.11 × 10-61
 
< 0.1%
1.12 × 10-62
 
0.1%
1.16 × 10-61
 
< 0.1%
1.22 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.9761
< 0.1%
0.9681
< 0.1%
0.9641
< 0.1%
0.9581
< 0.1%
0.9571
< 0.1%
0.9561
< 0.1%
0.9551
< 0.1%
0.9542
0.1%
0.9521
< 0.1%
0.9421
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.342588002
Minimum0
Maximum11
Zeros216
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:13.504141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.648240477
Coefficient of variation (CV)0.6828601561
Kurtosis-1.338827396
Mean5.342588002
Median Absolute Deviation (MAD)3
Skewness-0.00935995938
Sum10776
Variance13.30965858
MonotonicityNot monotonic
2022-03-22T18:41:13.705681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1257
12.7%
0216
10.7%
7212
10.5%
9191
9.5%
11187
9.3%
2184
9.1%
5166
8.2%
6159
7.9%
10141
7.0%
8136
6.7%
Other values (2)168
8.3%
ValueCountFrequency (%)
0216
10.7%
1257
12.7%
2184
9.1%
363
 
3.1%
4105
5.2%
5166
8.2%
6159
7.9%
7212
10.5%
8136
6.7%
9191
9.5%
ValueCountFrequency (%)
11187
9.3%
10141
7.0%
9191
9.5%
8136
6.7%
7212
10.5%
6159
7.9%
5166
8.2%
4105
5.2%
363
 
3.1%
2184
9.1%

liveness
Real number (ℝ≥0)

Distinct793
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1908440258
Minimum0.0188
Maximum0.969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:13.950213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0188
5-th percentile0.05674
Q10.0923
median0.127
Q30.247
95-th percentile0.538
Maximum0.969
Range0.9502
Interquartile range (IQR)0.1547

Descriptive statistics

Standard deviation0.155453165
Coefficient of variation (CV)0.8145560983
Kurtosis4.095643847
Mean0.1908440258
Median Absolute Deviation (MAD)0.0484
Skewness1.952703491
Sum384.9324
Variance0.02416568651
MonotonicityNot monotonic
2022-03-22T18:41:14.229110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10122
 
1.1%
0.11621
 
1.0%
0.11120
 
1.0%
0.10519
 
0.9%
0.11419
 
0.9%
0.10419
 
0.9%
0.12219
 
0.9%
0.11318
 
0.9%
0.10918
 
0.9%
0.10317
 
0.8%
Other values (783)1825
90.5%
ValueCountFrequency (%)
0.01881
< 0.1%
0.02191
< 0.1%
0.0221
< 0.1%
0.02341
< 0.1%
0.0261
< 0.1%
0.02631
< 0.1%
0.02711
< 0.1%
0.02731
< 0.1%
0.02881
< 0.1%
0.02931
< 0.1%
ValueCountFrequency (%)
0.9691
< 0.1%
0.9631
< 0.1%
0.9531
< 0.1%
0.9241
< 0.1%
0.911
< 0.1%
0.8972
0.1%
0.8871
< 0.1%
0.8761
< 0.1%
0.8731
< 0.1%
0.8191
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1808
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.085624194
Minimum-33.097
Maximum-0.307
Zeros0
Zeros (%)0.0%
Negative2017
Negative (%)100.0%
Memory size15.9 KiB
2022-03-22T18:41:14.501211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-33.097
5-th percentile-14.1526
Q1-8.394
median-6.248
Q3-4.746
95-th percentile-3.0672
Maximum-0.307
Range32.79
Interquartile range (IQR)3.648

Descriptive statistics

Standard deviation3.761684275
Coefficient of variation (CV)-0.5308896113
Kurtosis7.908472702
Mean-7.085624194
Median Absolute Deviation (MAD)1.732
Skewness-2.226556084
Sum-14291.704
Variance14.15026858
MonotonicityNot monotonic
2022-03-22T18:41:14.742147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.3794
 
0.2%
-7.4163
 
0.1%
-8.6923
 
0.1%
-6.9643
 
0.1%
-5.5343
 
0.1%
-7.3143
 
0.1%
-6.2383
 
0.1%
-7.0733
 
0.1%
-7.7753
 
0.1%
-7.4453
 
0.1%
Other values (1798)1986
98.5%
ValueCountFrequency (%)
-33.0971
< 0.1%
-31.3671
< 0.1%
-31.0821
< 0.1%
-30.4471
< 0.1%
-29.461
< 0.1%
-27.351
< 0.1%
-26.9241
< 0.1%
-25.7661
< 0.1%
-25.7561
< 0.1%
-25.3581
< 0.1%
ValueCountFrequency (%)
-0.3071
< 0.1%
-0.7181
< 0.1%
-0.7871
< 0.1%
-0.9351
< 0.1%
-0.9941
< 0.1%
-1.1571
< 0.1%
-1.1881
< 0.1%
-1.2581
< 0.1%
-1.3671
< 0.1%
-1.3741
< 0.1%

mode
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.9 KiB
1
1235 
0
782 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
11235
61.2%
0782
38.8%

Length

2022-03-22T18:41:14.976614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T18:41:15.132367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
11235
61.2%
0782
38.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

speechiness
Real number (ℝ≥0)

Distinct792
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09266425384
Minimum0.0231
Maximum0.816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:15.254625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0231
5-th percentile0.0289
Q10.0375
median0.0549
Q30.108
95-th percentile0.31
Maximum0.816
Range0.7929
Interquartile range (IQR)0.0705

Descriptive statistics

Standard deviation0.08993146446
Coefficient of variation (CV)0.9705086992
Kurtosis6.260200685
Mean0.09266425384
Median Absolute Deviation (MAD)0.0218
Skewness2.309580743
Sum186.9038
Variance0.0080876683
MonotonicityNot monotonic
2022-03-22T18:41:15.508744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10211
 
0.5%
0.037811
 
0.5%
0.05410
 
0.5%
0.03349
 
0.4%
0.03459
 
0.4%
0.03359
 
0.4%
0.03539
 
0.4%
0.03639
 
0.4%
0.1119
 
0.4%
0.03228
 
0.4%
Other values (782)1923
95.3%
ValueCountFrequency (%)
0.02312
0.1%
0.02321
< 0.1%
0.02331
< 0.1%
0.02341
< 0.1%
0.02371
< 0.1%
0.02391
< 0.1%
0.02451
< 0.1%
0.02461
< 0.1%
0.02521
< 0.1%
0.02552
0.1%
ValueCountFrequency (%)
0.8161
< 0.1%
0.6221
< 0.1%
0.5481
< 0.1%
0.5421
< 0.1%
0.4881
< 0.1%
0.4841
< 0.1%
0.4821
< 0.1%
0.4742
0.1%
0.4551
< 0.1%
0.4472
0.1%

tempo
Real number (ℝ≥0)

Distinct1919
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.6032717
Minimum47.859
Maximum219.331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:15.769058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum47.859
5-th percentile80.9534
Q1100.189
median121.427
Q3137.849
95-th percentile171.9306
Maximum219.331
Range171.472
Interquartile range (IQR)37.66

Descriptive statistics

Standard deviation26.68560373
Coefficient of variation (CV)0.2194480737
Kurtosis0.04160001835
Mean121.6032717
Median Absolute Deviation (MAD)18.543
Skewness0.4390583734
Sum245273.799
Variance712.1214465
MonotonicityNot monotonic
2022-03-22T18:41:16.057825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.9953
 
0.1%
121.993
 
0.1%
119.9993
 
0.1%
128.0143
 
0.1%
128.0043
 
0.1%
126.0123
 
0.1%
123.9663
 
0.1%
79.9522
 
0.1%
97.9672
 
0.1%
115.0762
 
0.1%
Other values (1909)1990
98.7%
ValueCountFrequency (%)
47.8591
< 0.1%
59.3851
< 0.1%
60.3911
< 0.1%
60.4831
< 0.1%
61.7581
< 0.1%
63.6371
< 0.1%
63.9381
< 0.1%
64.971
< 0.1%
64.9921
< 0.1%
65.0031
< 0.1%
ValueCountFrequency (%)
219.3311
< 0.1%
209.6861
< 0.1%
207.9691
< 0.1%
203.8221
< 0.1%
202.0131
< 0.1%
200.7491
< 0.1%
200.0351
< 0.1%
199.9881
< 0.1%
199.7271
< 0.1%
199.5191
< 0.1%

time_signature
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.9 KiB
4.0
1891 
3.0
 
93
5.0
 
32
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.01891
93.8%
3.093
 
4.6%
5.032
 
1.6%
1.01
 
< 0.1%

Length

2022-03-22T18:41:16.322946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T18:41:16.637996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
4.01891
93.8%
3.093
 
4.6%
5.032
 
1.6%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

valence
Real number (ℝ≥0)

Distinct853
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4968150223
Minimum0.0348
Maximum0.992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2022-03-22T18:41:16.794273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0348
5-th percentile0.111
Q10.295
median0.492
Q30.691
95-th percentile0.9092
Maximum0.992
Range0.9572
Interquartile range (IQR)0.396

Descriptive statistics

Standard deviation0.2471954687
Coefficient of variation (CV)0.4975603748
Kurtosis-1.01058794
Mean0.4968150223
Median Absolute Deviation (MAD)0.198
Skewness0.07838968431
Sum1002.0759
Variance0.06110559976
MonotonicityNot monotonic
2022-03-22T18:41:17.075353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.169
 
0.4%
0.3729
 
0.4%
0.3318
 
0.4%
0.7698
 
0.4%
0.528
 
0.4%
0.3547
 
0.3%
0.4417
 
0.3%
0.6687
 
0.3%
0.5617
 
0.3%
0.3087
 
0.3%
Other values (843)1940
96.2%
ValueCountFrequency (%)
0.03481
< 0.1%
0.03591
< 0.1%
0.03732
0.1%
0.03781
< 0.1%
0.03841
< 0.1%
0.0391
< 0.1%
0.03971
< 0.1%
0.03991
< 0.1%
0.0441
< 0.1%
0.04512
0.1%
ValueCountFrequency (%)
0.9921
 
< 0.1%
0.9751
 
< 0.1%
0.9741
 
< 0.1%
0.9732
0.1%
0.9722
0.1%
0.9711
 
< 0.1%
0.9681
 
< 0.1%
0.9674
0.2%
0.9662
0.1%
0.9653
0.1%

target
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.9 KiB
1
1020 
0
997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11020
50.6%
0997
49.4%

Length

2022-03-22T18:41:17.333601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-22T18:41:17.499234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
11020
50.6%
0997
49.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

song_title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1956
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size15.9 KiB
River
 
3
Jack
 
3
Mask Off
 
2
Be My Baby
 
2
Oblivion
 
2
Other values (1951)
2005 

Length

Max length120
Median length14
Mean length17.19137333
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1897 ?
Unique (%)94.1%

Sample

1st rowMask Off
2nd rowRedbone
3rd rowXanny Family
4th rowMaster Of None
5th rowParallel Lines

Common Values

ValueCountFrequency (%)
River3
 
0.1%
Jack3
 
0.1%
Mask Off2
 
0.1%
Be My Baby2
 
0.1%
Oblivion2
 
0.1%
Obedear2
 
0.1%
Swimming Pools (Drank) - Extended Version2
 
0.1%
Pyramids2
 
0.1%
1-800-273-82552
 
0.1%
Midnight City2
 
0.1%
Other values (1946)1995
98.9%

Length

2022-03-22T18:41:17.659325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
293
 
4.4%
the187
 
2.8%
you141
 
2.1%
feat127
 
1.9%
i105
 
1.6%
me87
 
1.3%
remix83
 
1.2%
love77
 
1.1%
of75
 
1.1%
a68
 
1.0%
Other values (2504)5485
81.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

artist
Categorical

HIGH CARDINALITY

Distinct1343
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Memory size15.9 KiB
Drake
 
16
Rick Ross
 
13
Disclosure
 
12
WALK THE MOON
 
10
Backstreet Boys
 
10
Other values (1338)
1956 

Length

Max length82
Median length11
Mean length10.97372335
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1002 ?
Unique (%)49.7%

Sample

1st rowFuture
2nd rowChildish Gambino
3rd rowFuture
4th rowBeach House
5th rowJunior Boys

Common Values

ValueCountFrequency (%)
Drake16
 
0.8%
Rick Ross13
 
0.6%
Disclosure12
 
0.6%
WALK THE MOON10
 
0.5%
Backstreet Boys10
 
0.5%
Crystal Castles9
 
0.4%
FIDLAR9
 
0.4%
Future8
 
0.4%
Fall Out Boy8
 
0.4%
Skrillex8
 
0.4%
Other values (1333)1914
94.9%

Length

2022-03-22T18:41:17.955066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the145
 
3.8%
51
 
1.3%
of18
 
0.5%
brown17
 
0.4%
young17
 
0.4%
drake16
 
0.4%
chris15
 
0.4%
dj14
 
0.4%
boys14
 
0.4%
j14
 
0.4%
Other values (1982)3516
91.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-22T18:41:06.280325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:39.754311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:42.241014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:44.611684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:46.748591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:48.951961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:51.719512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:54.230554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:56.600140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:58.933681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:01.260852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:03.899609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:06.465445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:40.035181image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:42.426941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:44.806299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:46.949563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:49.146957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:51.915078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:54.418340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:56.846411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:59.127577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:01.488094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:04.133912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:06.673982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:40.210855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:42.611122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:44.978811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:47.122162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:49.333512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:52.101886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:54.584918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:57.023677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:59.304367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:01.694978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:04.349425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:06.865268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:40.435591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:42.779766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:45.156012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:47.290748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:49.691771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:52.296159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:54.751283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:57.186875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:59.485318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:02.074870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:04.554426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:07.057179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:40.636751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:42.965277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:45.341617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:47.476394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:50.004411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:52.494295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:54.926734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:57.374359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:59.669009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:02.265348image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:04.762419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:07.245717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:40.840330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:43.137641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:45.513692image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:47.643127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:50.242635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:52.702222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:55.095162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:57.604502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:59.845945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:02.451536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:04.959747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:07.443401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:41.039537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:43.323312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:45.687056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:47.820798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:50.472329image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:52.916066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:55.274304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:40:57.781146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:00.027574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:02.647582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:05.197973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-03-22T18:41:07.625772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2022-03-22T18:41:06.108971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-22T18:41:18.581189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-22T18:41:18.900439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-22T18:41:19.189378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-22T18:41:19.383854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-22T18:41:08.878847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-22T18:41:09.519977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencetargetsong_titleartist
000.010200.8332046000.4340.02190020.1650-8.79510.4310150.0624.00.2861Mask OffFuture
110.199000.7433269330.3590.00611010.1370-10.40110.0794160.0834.00.5881RedboneChildish Gambino
220.034400.8381857070.4120.00023420.1590-7.14810.289075.0444.00.1731Xanny FamilyFuture
330.604000.4941994130.3380.51000050.0922-15.23610.026186.4684.00.2301Master Of NoneBeach House
440.180000.6783928930.5610.51200050.4390-11.64800.0694174.0044.00.9041Parallel LinesJunior Boys
550.004790.8042513330.5600.00000080.1640-6.68210.185085.0234.00.2641Sneakin’Drake
660.014500.7392414000.4720.00000710.2070-11.20410.156080.0304.00.3081Childs PlayDrake
770.020200.2663496670.3480.664000100.1600-11.60900.0371144.1544.00.3931Gyöngyhajú lányOmega
880.048100.6032028530.9440.000000110.3420-3.62600.3470130.0354.00.3981I've Seen FootageDeath Grips
990.002080.8362268400.6030.00000070.5710-7.79210.237099.9944.00.3861Digital AnimalHoney Claws

Last rows

idacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencetargetsong_titleartist
200720070.0021700.6392962760.9780.112000110.0838-2.37610.0730144.9864.00.4940Let It Go - Scott Melker & Mister Gray RemixNERVO
200820080.0541000.6461604080.7140.00000000.1340-6.51110.037897.9574.00.5890Call On Me - EDWYNN X TIKAL, Spirix RemixStarley
200920090.0049900.5952240000.8370.03590010.1010-6.01110.0696149.9644.00.3310AamonKuuro
201020100.0023100.6242060130.9710.12100060.2550-0.93500.0643102.0034.00.4420Hey Baby - Steve Aoki RemixDimitri Vegas & Like Mike
201120110.0005860.5282450530.8790.00489060.0432-5.89100.1200128.2684.00.3270Brightside - Borgeous RemixIcona Pop
201220120.0010600.5842744040.9320.00269010.1290-3.50110.333074.9764.00.2110Like A Bitch - Kill The Noise RemixKill The Noise
201320130.0877000.8941821820.8920.00167010.0528-2.66310.1310110.0414.00.8670CandyDillon Francis
201420140.0085700.6372072000.9350.00399000.2140-2.46710.1070150.0824.00.4700Habit - Dack Janiels & Wenzday RemixRain Man
201520150.0016400.5571856000.9920.67700010.0913-2.73510.1330150.0114.00.6230First ContactTwin Moons
201620160.0028100.4462045200.9150.00003990.2180-6.22110.1410190.0134.00.4020I Wanna Get BetterBleachers